By jjallaire
(Post by Dirk Eddelbuettel and JJ Allaire)
A common theme over the last few decades was that we could afford to simply sit back and let computer (hardware) engineers take care of increases in computing speed thanks to Moore’s law. That same line of thought now frequently points out that we are getting closer and closer to the physical limits of what Moore’s law can do for us.
So the new best hope is (and has been) parallel processing. Even our smartphones have multiple cores, and most if not all retail PCs now possess two, four or more cores. Real computers, aka somewhat decent servers, can be had with 24, 32 or more cores as well, and all that is before we even consider GPU coprocessors or other upcoming changes.
Sometimes our tasks are embarrassingly simple as is the case with many data-parallel jobs: we can use higher-level operations such as those offered by the base R package parallel to spawn multiple processing tasks and gather the results. Dirk covered all this in some detail in previous talks on High Performance Computing with R (and you can also consult the CRAN Task View on High Performance Computing with R).
But sometimes we cannot use data-parallel approaches. Hence we have to redo our algorithms. Which is really hard. R itself has been relying on the (fairly mature) OpenMP standard for some of its operations. Luke Tierney’s keynote at the 2014 R/Finance conference mentioned some of the issues related to OpenMP, which works really well on Linux but currently not so well on other platforms. R is expected to make wider use of it in future versions once compiler support for OpenMP on Windows and OS X improves.
In the meantime, the RcppParallel package provides a complete toolkit for creating portable, high-performance parallel algorithms without requiring direct …read more
Source:: r-bloggers.com